We address the questions of identifying pairs of interacting neurons from the observation of their spiking activity. The neuronal network is modeled by a system of interacting point processes with memory of variable length. The influence of a neuron on another can be either excitatory or inhibitory. To identify the existence and the nature of an interaction we propose an algorithm based only on the observation of joint activity of the two neurons in successive time slots. This reduces the amount of computation and storage required to run the algorithm, thereby making the algorithm suitable for the analysis of real neuronal data sets. We obtain computable upper bounds for the probabilities of false positive and false negative detection. As a corollary we prove the consistency of the identification algorithm.

翻译：我们从观测神经神经元活动中找出互动神经元的对子。神经网络由具有可变长度内存的交互点进程系统模拟。神经元对另一个神经元的影响可以是刺激性的,也可以是抑制性的。为了确定互动的存在和性质,我们建议一种算法,其依据只是观察两个神经元在连续的时间空档中的联合活动。这减少了算法运行所需的计算和存储量,从而使算法适合于分析真实神经元数据集。我们获得了虚假正反检测概率的可计算上限。作为必然结果,我们证明了识别算法的一致性。